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Results for “Integrated proteomics”

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PubMed2025

Integrating Imaging-Derived Clinical Endotypes with Plasma Proteomics and External Polygenic Risk Scores Enhances Coronary Microvascular Disease Risk Prediction.

Coronary microvascular disease (CMVD) is an underdiagnosed but significant contributor to the burden of ischemic heart disease, characterized by angina and myocardial infarction. The development of risk prediction models such as polygenic risk scores (PRS) for CMVD has been limited by a lack of large-scale genome-wide association studies (GWAS). However, there is significant overlap between CMVD and enrollment criteria for coronary artery disease (CAD) GWAS. In this study, we developed CMVD PRS models by selecting variants identified in a CMVD GWAS and applying weights from an external CAD GWAS, using CMVD-associated loci as proxies for the genetic risk. We integrated plasma proteomics, clinical measures from perfusion PET imaging, and PRS to evaluate their contributions to CMVD risk prediction in comprehensive machine and deep learning models. We then developed a novel unsupervised endotyping framework for CMVD from perfusion PET-derived myocardial blood flow data, revealing distinct patient subgroups beyond traditional case-control definitions. This imaging-based stratification substantially improved classification performance alongside plasma proteomics and PRS, achieving AUROCs between 0.65 and 0.73 per class, significantly outperforming binary classifiers and existing clinical models, highlighting the potential of this stratification approach to enable more precise and personalized diagnosis by capturing the underlying heterogeneity of CMVD. This work represents the first application of imaging-based endotyping and the integration of genetic and proteomic data for CMVD risk prediction, establishing a framework for multimodal modeling in complex diseases.

Cardiovascular Disease↗

PubMed2006

Molecular interactions between the specialist herbivore Manduca sexta (Lepidoptera, Sphingidae) and its natural host Nicotiana attenuata. VII. Changes in the plant's proteome.

When Manduca sexta attacks Nicotiana attenuata, fatty acid-amino acid conjugates (FACs) in the larvae's oral secretions (OS) are introduced into feeding wounds. These FACs trigger a transcriptional response that is similar to the response induced by insect damage. Using two-dimensional gel electrophoresis, matrix-assisted laser desorption ionization-time of flight, and liquid chromatography-tandem mass spectrometry, we characterized the proteins in phenolic extracts and in a nuclear fraction of leaves elicited by larval attack, and/or in leaves wounded and treated with OS, FAC-free OS, and synthetic FACs. Phenolic extracts yielded approximately 600 protein spots, many of which were altered by elicitation, whereas nuclear protein fractions yielded approximately 100 spots, most of which were unchanged by elicitation. Reproducible elicitor-induced changes in 90 spots were characterized. In general, proteins that increased were involved in primary metabolism, defense, and transcriptional and translational regulation; those that decreased were involved in photosynthesis. Like the transcriptional defense responses, proteomic changes were strongly elicited by the FACs in OS. A semiquantitative reverse transcription-PCR approach based on peptide sequences was used to compare transcript and protein accumulation patterns for 17 candidate proteins. In six cases the patterns of elicited transcript accumulation were consistent with those of elicited protein accumulation. Functional analysis of one of the identified proteins involved in photosynthesis, RuBPCase activase, was accomplished by virus-induced gene silencing. Plants with decreased levels of RuBPCase activase protein had reduced photosynthetic rates and RuBPCase activity, and less biomass, responses consistent with those of herbivore-attacked plants. We conclude that the response of the plant's proteome to herbivore elicitation is complex, and integrated transcriptome-proteome-metabolome analysis is required to fully understand this ubiquitous ecological interaction.

Animals↗

PubMed2005

A molecular model for axon guidance based on cross talk between rho GTPases.

To systematically understand the molecular events that underlie biological phenomena, we must develop methods to integrate an enormous amount of genomic and proteomic data. The integration of molecular data should go beyond the construction of biochemical cascades among molecules to include tying the biochemical phenomena to physical events. For the behavior and guidance of growth cones, it remains largely unclear how biochemical events in the cytoplasm are linked to the morphological changes of the growth cone. We take a computational approach to simulate the biochemical signaling cascade involving members of the Rho family of GTPases and examine their potential roles in growth-cone motility and axon guidance. Based on the interactions between Cdc42, Rac, and RhoA, we show that the activation of a Cdc42-specific GEF resulted in switching responses between oscillatory and convergent activities for all three GTPases. We propose that the switching responses of these GTPases are the molecular basis for the decision mechanism that determines the direction of the growth-cone expansion, providing a spatiotemporal integration mechanism that allows the growth cone to detect small gradients of external guidance cues. These results suggest a potential role for the cross talk between Rho GTPases in governing growth-cone movement and axon guidance and underscore the link between chemodynamic reactions and cellular behaviors.

Animals↗

PubMed2006

Human protein reference database--2006 update.

Human Protein Reference Database (HPRD) (http://www.hprd.org) was developed to serve as a comprehensive collection of protein features, post-translational modifications (PTMs) and protein-protein interactions. Since the original report, this database has increased to >20 000 proteins entries and has become the largest database for literature-derived protein-protein interactions (>30 000) and PTMs (>8000) for human proteins. We have also introduced several new features in HPRD including: (i) protein isoforms, (ii) enhanced search options, (iii) linking of pathway annotations and (iv) integration of a novel browser, GenProt Viewer (http://www.genprot.org), developed by us that allows integration of genomic and proteomic information. With the continued support and active participation by the biomedical community, we expect HPRD to become a unique source of curated information for the human proteome and spur biomedical discoveries based on integration of genomic, transcriptomic and proteomic data.

Databases, Protein↗

PubMed2026

Proteogenomic features define subtypes of mantle cell lymphoma.

Mantle cell lymphoma (MCL) is a biologically heterogeneous B-cell malignancy. Although genomics and transcriptomics have delineated parts of the MCL disease spectrum, proteomics remains largely unexplored. Here, we conducted a comprehensive proteogenomic analysis integrating genomics, transcriptomics, and proteomics on peripheral blood samples from 27 patients with MCL and 4 healthy donors to investigate the translational and posttranslational dimensions of MCL. Our study identified 1296 downregulated and 468 upregulated proteins in MCL cells. The splicing pathways were significantly upregulated at both the mRNA and protein levels, suggesting a critical role for aberrant RNA splicing in MCL pathogenesis. Integration of proteomic data with genetic aberrations revealed immunoglobulin heavy chain variable mutational status and CCND1 mutation are associated with distinctive transcriptomic and proteomic profiles, which correspond to significant differences in clinical outcomes. A multiomics molecular stratification model incorporating proteomic data showed superior predictive power for patient survival compared with single-omics models (concordance index, 0.83 vs 0.74). This study provides, to our knowledge, the first comprehensive proteogenomic profile of MCL, offering novel insights into its molecular mechanisms and clinical behavior. The identification of molecular subtypes and prognostic protein signatures underscores the potential of proteomics to guide precision medicine strategies for MCL.

Humans↗

PubMed2026

Integrated analysis of gut microbiota, serum metabolomics, and proteomics reveals novel associations with clinical symptoms in patients with cerebral infarction.

BACKGROUND: Cerebral infarction (CI) is a major cause of adult disability and mortality worldwide. Mounting evidence supports the critical role of the gut-brain axis in cerebrovascular disease progression. This study aimed to characterize the alterations in gut microbiota, serum metabolome, and serum proteome in patients with CI, and to identify multi-omics signatures associated with clinical symptoms. METHODS: A total of 20 CI patients and 20 healthy controls (HC) were enrolled. Fecal microbiota was profiled using 16 S rRNA gene high-throughput sequencing. Serum metabolomics and proteomics were analyzed using ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and data-independent acquisition (DIA) proteomics, respectively. Spearman correlation and multi-omics integration were applied to explore the associations among microbiota, metabolites, proteins, and clinical indicators. RESULTS: CI patients displayed significant gut microbiota dysbiosis, with a markedly lower gut microbiota health index (GMHI) and higher microbiota disorder index (MDI) compared with HC (P < 0.001). The genera g_norank_o_RF39 and Oxalobacter were significantly enriched in CI patients, whereas Clostridium_sensu_stricto_1 and Agathobacter were enriched in HC. Metabolomic analysis identified 445 differential metabolites, mainly involved in glycerophospholipid metabolism, phenylalanine metabolism, and caffeine metabolism. Proteomic analysis revealed 140 differentially expressed proteins linked to inflammatory responses, calcium signaling, and NF-κB signaling. Multi-omics integration showed that signature gut microbiota was strongly correlated (P < 0.005) with key serum metabolites and proteins implicated in CI pathogenesis. CONCLUSIONS: This integrated multi-omics study revealed distinct gut microbiota, serum metabolomic, and proteomic alterations in CI patients. The microbiota-metabolite-protein regulatory axes provide novel insights into the gut-brain axis in CI and may serve as potential diagnostic biomarkers or therapeutic targets.

Humans↗

PubMed2026

Single-organ proteomics in Drosophila melanogaster larva.

The combination of genetic accessibility, organ complexity, evolutionary conservation, and cost-efficiency makes Drosophila melanogaster (Dm) a well-known model system for biomedical and fundamental biological research. Proteomic analysis of single organs enables the identification and quantification of proteins expressed in specific organs. This will help to uncover specific biological functions and unique protein profiles that are not detectable in whole-organism analyses. In this study we have isolated single organs form Dm larvae, and we have performed a deep proteomics mapping by following a minimal manipulation preparation procedure. The combined dataset across all organs comprised 9132 identified proteins. As anticipated, principal component analysis (PCA) revealed clear separation between the proteomes of most organs, confirming distinct protein profiles. These findings demonstrate the applicability of the sample preparation strategy for high-resolution proteomic characterization of individual organs in Drosophila. Given the extensive genetic tools available for this model organism, our approach has the potential to open new avenues for proteomic studies in Drosophila melanogaster and any other biological systems where the sample amount is limiting. SIGNIFICANCE STATEMENT: Drosophila melanogaster is a well-known model system for biomedical and fundamental biological research that serves as a valuable in vivo model organism due to its high degree of evolutionary conservation with higher vertebrates, tractable genetics, and logistical efficiency. However, the proteome of Drosophila at single organ level has been elusive to date, due to several factors like low sensitivity of previous generation mass spectrometers and sample preparation procedures, difficult isolation of some organs. In this study we have applied a compilation of advanced methods including minimal sample manipulation together with simple, straightforward and efficient protein extraction and digestion methods. Obtained peptides were minimally handled to be analyzed by applying specific and sensitive nLC methods coupled on-line to state-of-the-art MS/MS system. Altogether, the applied strategy allowed us to get the first single organ study to date for this animal. These datasets represent a significative resource for future genomic, transcriptomic and proteomic studies in Drosophila, as multi-omic integration requires deep proteomics to translate data into functional biochemistry, and serves as a critical bridge and an indispensable standalone resource across the genomic, transcriptomic, and proteomic landscapes.

Animals↗

PubMed2026

ARCADIA reveals spatially dependent transcriptional programs through integration of scRNA-seq and spatial proteomics.

MOTIVATION: Cellular states are strongly influenced by spatial context, but single-cell RNA sequencing (scRNA-seq) loses information about local tissue organization, while spatial proteomic assays capture limited marker panels that constrain transcriptomic inference. Integrating these modalities can elucidate how spatial niches shape transcriptional programs, yet existing approaches depend on either feature-level correspondence such as gene-protein linkage or cell-level barcode pairing, which is often unavailable. RESULTS: We present ARCADIA (ARchetype-based Clustering and Alignment with Dual Integrative Autoencoders), a generative framework for cross-modal integration that operates without cell barcode pairing and does not assume direct feature-to-feature correspondence. ARCADIA identifies modality-specific archetypes, that is, convex combinations of cells representing extreme phenotypic states, and aligns these anchors across modalities by minimizing the discrepancy between their cell-type composition profiles. The aligned archetypes define a shared coordinate system that anchors dual variational autoencoders (VAEs) trained with cross-modal geometric regularization, preserving archetype structure and spatial neighborhood information while enabling bidirectional translation between modalities. On semi-synthetic CITE-seq data, ARCADIA outperforms existing weak-linkage methods. Applied to independent human tonsil scRNA-seq and CODEX data, ARCADIA reconstructs known tissue architecture and reveals spatially dependent transcriptional programs linking B-cell maturation and T-cell activation or exhaustion to microenvironmental niches. AVAILABILITY AND IMPLEMENTATION: Source code is accessible at https://github.com/azizilab/ARCADIA_public. Reproducibility scripts and data are available at https://github.com/azizilab/arcadia_reproducibility.

Proteomics↗

PubMed2025

Algorithms and tools for data-driven omics integration to achieve multilayer biological insights: a narrative review.

Systems biology is a holistic approach to biological sciences that combines experimental and computational strategies, aimed at integrating information from different scales of biological processes to unravel pathophysiological mechanisms and behaviours. In this scenario, high-throughput technologies have been playing a major role in providing huge amounts of omics data, whose integration would offer unprecedented possibilities in gaining insights on diseases and identifying potential biomarkers. In the present review, we focus on strategies that have been applied in literature to integrate genomics, transcriptomics, proteomics, and metabolomics in the year range 2018-2024. Integration approaches were divided into three main categories: statistical-based approaches, multivariate methods, and machine learning/artificial intelligence techniques. Among them, statistical approaches (mainly based on correlation) were the ones with a slightly higher prevalence, followed by multivariate approaches, and machine learning techniques. Integrating multiple biological layers has shown great potential in uncovering molecular mechanisms, identifying putative biomarkers, and aid classification, most of the time resulting in better performances when compared to single omics analyses. However, significant challenges remain. The high-throughput nature of omics platforms introduces issues such as variable data quality, missing values, collinearity, and dimensionality. These challenges further increase when combining multiple omics datasets, as the complexity and heterogeneity of the data increase with integration. We report different strategies that have been found in literature to cope with these challenges, but some open issues still remain and should be addressed to disclose the full potential of omics integration.

Algorithms↗

PubMed2004

Integrated analysis of genetic, genomic and proteomic data.

The rapid expansion of methods for measuring biological data ranging from DNA sequence variations to mRNA expression and protein abundance presents the opportunity to utilize multiple types of information jointly in the study of human health and disease. Organisms are complex systems that integrate inputs at myriad levels to arrive at an observable phenotype. Therefore, it is essential that questions concerning the etiology of phenotypes as complex as common human diseases take the systemic nature of biology into account, and integrate the information provided by each data type in a manner analogous to the operation of the body itself. While limited in scope, the initial forays into the joint analysis of multiple data types have yielded interesting results that would not have been reached had only one type of data been considered. These early successes, along with the aforementioned theoretical appeal of data integration, provide impetus for the development of methods for the parallel, high-throughput analysis of multiple data types. The idea that the integrated analysis of multiple data types will improve the identification of biomarkers of clinical endpoints, such as disease susceptibility, is presented as a working hypothesis.

Animals↗

PubMed2026

Trans-omics integration underscores distinct roles of polyunsaturated phospholipids in bidirectional offspring birth weight deviations.

BACKGROUND: Abnormal birth weights are associated with adverse pregnancy outcomes and future metabolic consequences. We aimed to examine cord blood lipidomes from low, normal and high birth weight (LBW, NBW, HBW) infants to identify core lipid signatures associated with non-optimum birth weight, and to derive biological insights through trans-omics data integration with placental proteome, maternal plasma lipidome and clinical phenome. METHODS: We conducted quantitative lipidomics of cord blood samples from two independent cohorts: a retrospective discovery cohort (n = 147) and a prospective validation cohort (n = 73). Integration with placental proteomics, maternal plasma lipidomics and clinical phenomics was conducted to elucidate potential biological implications. FINDINGS: We identified substantial reductions in cord blood polyunsaturated phospholipids (PUFA-PLs) (FDR <0.05) associated with placental vesicle trafficking and formation in LBW, and altered neutrophil degranulation in HBW. Combinatorial analyses of paired maternal plasma and cord blood samples indicated that cord blood PUFA-PL reductions were not attributable to deficient maternal supply, but rather to impeded assimilation (LBW) and increased utilisation (HBW). INTERPRETATION: Our findings provide biological insights that may inform targetable, lipid-oriented nutritional and/or pharmacological strategies to modulate foetal growth and development, with the goal of optimising clinical outcomes for both mother and child. FUNDING: This work was supported by the National Natural Science Foundation of China (82170854, 81870579, 81870545, 82571043, 2357308); National High Level Hospital Clinical Research Funding (2022-PUMCH-C-019); Noncommunicable Chronic Diseases-National Science and Technology Major Project (2024ZD0530200 and 2024ZD0530204); Beijing Municipal Science & Technology Commission (Z201100005520011); Peking University Clinical Scientist Training Program (No. BMU2023PYJH022); Beijing Municipal Natural Science Foundation (7202163, 7184252).

Humans↗

PubMed2025

Core passive and facultative mTOR-mediated mechanisms coordinate mammalian protein synthesis and decay.

The maintenance of cellular homeostasis requires tight regulation of proteome concentration and composition. To achieve this, protein production and elimination must be robustly coordinated. However, the mechanistic basis of this coordination remains unclear. Here, we address this question using quantitative live-cell imaging, computational modeling, transcriptomics, and proteomics approaches. We found that protein decay rates systematically adapt to global alterations of protein synthesis rates. This adaptation is driven by a core passive mechanism supplemented by facultative changes in mechanistic/mammalian target of rapamycin (mTOR) signaling. Passive adaptation hinges on changes in the production rate of the machinery governing protein decay and allows for partial maintenance of the cellular proteome. Sustained changes in mTOR signaling provide an additional layer of adaptation unique to naive pluripotent stem cells, allowing for near-perfect maintenance of proteome composition. Our work unravels the mechanisms protecting the integrity of mammalian proteomes upon variations in protein synthesis rates. A record of this paper's transparent peer review process is included in the supplemental information.

TOR Serine-Threonine Kinases↗

PubMed2026

Bacterial stress responses lower mRNA-protein level correlations.

Diverse bacterial pathogens have evolved complex regulatory mechanisms to adapt to various environmental stresses during infection. The uncertainty in mRNA-protein levels in response to environmental stressors complicates our understanding of bacterial physiology and their adaptation to stressful environments. To examine this issue, we have integrated transcriptomics and proteomics data on three human bacterial pathogens: Salmonella enterica Typhimurium, Yersinia pseudotuberculosis, and Staphylococcus aureus under 10 infection-relevant stress conditions. We observed positive correlations between mRNA and protein levels, which were decreased under different stress conditions. Essential genes exhibited higher expression levels with lower variation across the conditions and stronger mRNA-protein correlations compared to nonessential genes, highlighting their critical role in bacterial adaptability and survival. Moreover, we identified a substantial number of genes with stress-induced noncorrelating mRNA-protein levels, particularly under conditions triggering strong stress responses. Particularly this level was dramatically lowered for osmotic stress-specific genes affected by impaired translational activity under osmotic stress. Our findings highlight the prevalence of noncorrelating mRNA-protein levels and the potential role of posttranslational modifications in modulating protein levels in response to environmental stressors during infection. This study provides a comprehensive framework for integrating transcriptomics and proteomics data and identifies potential gene products that might significantly impact the ability of diverse bacterial pathogens to adapt to hostile infection environments.

RNA, Messenger↗

PubMed2026

Integrated analysis of plasma metabolomics and proteomics reveals the biological characteristics of damp-heat and stasis-toxin syndrome in colorectal cancer.

OBJECTIVE: To investigate the biological attributes of core syndromes in colorectal cancer, namely, the damp-heat and stasis-toxin syndrome (SRYD). METHODS: Between October 2021 and October 2022, a cohort comprising 40 patients with colorectal cancer (CRC) diagnosed with damp-heat and stasis-toxin syndrome (SRYD group), 40 patients with CRC without this syndrome (non-SRYD group), and 40 healthy controls (Normal group) was recruited at Jiangsu Province Hospital of Chinese Medicine. Untargeted metabolomics analysis was conducted on plasma samples from all 120 participants, while differential protein analysis using four-dimensional data-independent acquisition proteomics was performed on 20 randomly selected samples per group. A combined analysis of proteomics and metabolomics data followed, and the identified potential diagnostic biomarkers were subsequently used to train and validate multiple machine learning models. RESULTS: Proteomic analysis revealed 130 differential proteins in the colorectal cancer with damp-heat and stasis-toxin syndrome (CRC-SRYD) group, enriched in pathways including complement and coagulation cascades, as well as nuclear factor kappa-B (NF-κB) signaling. Metabolomic analysis identified 584 differential metabolites within the same group, showing enrichment in pathways such as primary bile acid biosynthesis, central carbon metabolism in cancer, and glucagon signaling. Integrated pathway analysis indicated heightened activity of the NF-κB signaling pathway in the CRC-SRYD group. A biomarker panel, comprising 6 proteins and 9 metabolites selected through the ReliefF algorithm, was used to construct a diagnostic model with random forest, achieving an accuracy of 93.33%, sensitivity of 80.00%, and specificity of 100%. CONCLUSION: This study systematically elucidates plasma metabolomic and proteomic alterations in patients with CRC, establishing a robust diagnostic model for CRC syndrome (CRC-SRYD). Further investigation is warranted to clarify the underlying molecular mechanisms and biological foundations.

Humans↗

PubMedDate not supplied

Analysis of shotgun proteomics and RNA profiling data from Arabidopsis thaliana chloroplasts.

The integration of data from transcriptional profiling and shotgun proteomics experiments provides additional information about the identified proteins that goes beyond their plain detection. We have analyzed results from MS/MS shotgun detection of 426 Arabidopsis chloroplast proteins and genome-wide RNA profiling to identify correlations between gene expression, protein abundance and protein characteristics that influence their detection in high-throughput proteome analyses. The integrated data analysis revealed a significant molecular mass bias for the detection of proteins that were expressed at low transcript levels. Overall, the sequence coverage of most of the identified proteins increases with transcript levels indicating a positive correlation between transcript and relative protein abundance. This does not apply to a subset of the identified proteins suggesting specific properties that alter their detection in shotgun proteomics. This integrative comparison is a suitable strategy to validate large scale proteomics data and offers an assessment of the depth of the proteome analysis and the confidence in protein identification.

Arabidopsis↗

PubMed2006

GARBAN II: an integrative framework for extracting biological information from proteomic and genomic data.

Genomic and proteomic analyses generate a massive amount of data that requires specific bioinformatic tools for its management and interpretation. GARBAN II, developed from the previous GARBAN platform, provides an integrated framework to simultaneously analyse and compare multiple datasets from DNA microarrays and proteomic studies. The general architecture, gene classification and comparison, and graphical representation have been redesigned to ensure a user-friendly feature and to improve the capabilities and efficiency of this system. Additionally, GARBAN II has been extended with new applications to display networks of coexpressed genes and to integrate access to BioRag and MotifScanner so as to facilitate the holistic analysis of users' data.

Animals↗

PubMed2006

An integrated map of the murine hippocampal proteome based upon five mouse strains.

With the advent of proteomics technologies it is possible to simultaneously demonstrate the expression of hundreds of proteins. The information offered by proteomics provides context-based understanding of cellular protein networks and has been proven to be a valuable approach in neuroscience studies. The mouse hippocampus has been a major target of analysis in the search for molecular correlates to neuronal information storage. Although human and rat hippocampal samples have been successfully subjected to proteomic profiling, no elaborate analysis providing the fundamental experimental basis for protein-expression studies in the mouse hippocampus has been carried out as yet. This led us to construct a master map generated from the individual hippocampal proteomes of five different mouse strains. A proteomic approach, based upon 2-DE coupled to MS (MALDI-TOF/TOF) has been chosen in an attempt to establish a comprehensive reference database of proteins expressed in the mouse hippocampus. 469 individual proteins, represented by 1156 spots displaying various functional states of the respective gene products were identified. Proteomic profiling of the hippocampus, a brain region with a pivotal role for neuronal information processing and storage may provide insight into the characteristics of proteins serving this highly sophisticated function.

Animals↗

PubMed2005

Pharmacogenomics and cardiovascular drugs: need for integrated biological system with phenotypes and proteomic markers.

Personalized medicine is based on a better knowledge of biological variability, considering the important part due to genetics. When trying to identify involved genes and their products in differential cardiovascular drug responses, a five-step strategy is to be followed: 1) Pharmacokinetic-related genes and phenotypes (2) Pharmacodynamic targets, genes and products (3) Cardiovascular diseases and risks depending on specific or large metabolic cycles (4) Physiological variations of previously identified genes and proteins (5) Environment influences on them. After summarizing the most well-known genes involved in drug metabolism, we will take as example of drugs, the statins, considered as very important drugs from a Public-Health standpoint, but also for economical reasons. These drugs respond differently in human depending on multiple polymorphisms. We will give examples with common ApoE polymorphisms influencing the hypolipemic effects of statins. These drugs also have pleiotropic effects and decrease inflammatory markers. This illustrates the need to separate clinical diseases phenotypes in specific metabolic pathways, which could propose other classifications, of diseases and related genes. Hypertension is also a good example of clinical phenotype which should be followed after various therapeutic approaches by genes polymorphisms and proteins markers. Gene products are under clear environmental expression variations such as age, body mass index and obesity, alcohol, tobacco and dietary interventions which are the first therapeutical actions taken in cardiovascular diseases. But at each of the five steps, within a pharmacoproteomic strategy, we also need to use available information from peptides, proteins and metabolites, which usually are the gene products. A profiling approach, i.e., dealing with genomics, but now also with proteomics, is to be used. In conclusion, the profiling, as well as the large amount of data, will more than before render necessary an organized interpretation of DNA, RNA as well as proteins variations, both at individual and population level.

Cardiovascular Agents↗